Flow network model analysis

ABSTRACT

In one implementation, a method includes generating a manifold predictive model configured to calculate an initial virtual measurement associated with an oil field comprising a plurality of oil wells. The manifold predictive model can be based on one or more predictive models associated with one or more components of the oil field. The method also includes receiving data characterizing one or more pressure measurements and flow measurements obtained in the oil field. The method further includes determining a prospective sensor location of a first prospective sensor in the oil field. The first prospective sensor can be configured to detect an oil field parameter. The manifold predictive model can be configured to receive data characterizing the detected oil field parameter and generate an updated virtual measurement. The method also includes providing the prospective sensor location and the identity of the first prospective sensor.

RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/797,097 filed on Jan. 25, 2019,the entire contents of which are hereby expressly incorporated byreference herein.

BACKGROUND

Process values of process variables at a process plant (e.g., oil flowat an oil rig) can be tracked (e.g., at regular interval) to monitor theoperation of the plant. Observing the process variables can allow anoperator to ensure desirable operation of the plant. The process valuescan be measured, for example, by sensors (e.g., fluid flow meters,pressure gauges, thermocouples, accelerometers) located at the processplant. However, it may not be possible to detect values of all thedesirable processes and/or values of a process at multiple locations inthe process plant. This can be due to prohibitive cost of installingmultiple sensors. Additionally, sensors that can detect certainprocesses (e.g., multi-phase fluid flow) can be expensive.

Numerical simulation based on regression models can be used to predictprocess values that cannot be directly measured. The numericalsimulations can use process values measured by one or more sensors addedto the process plant as outputs of the regression models. Suchtechniques may not be accurate as they do not model the actual processesat the plant and can be prone to over fitting. Additionally, theseregression-based methods may require a large set of additional data forbuilding the regression model.

SUMMARY

In general, apparatus, systems, methods and articles of manufacture fordetermination of virtual process parameters are provided.

In one implementation, a method includes generating a manifoldpredictive model configured to calculate an initial virtual measurementassociated with an oil field comprising a plurality of oil wells. Themanifold predictive model can be based on one or more predictive modelsassociated with one or more components of the oil field. The method alsoincludes receiving data characterizing one or more pressure measurementsand flow measurements obtained in the oil field. The method furtherincludes determining a prospective sensor location of a firstprospective sensor in the oil field. The first prospective sensor can beconfigured to detect an oil field parameter. The manifold predictivemodel can be configured to receive data characterizing the detected oilfield parameter and generate an updated virtual measurement. The methodalso includes providing the prospective sensor location and the identityof the first prospective sensor.

One or more of the following features can be included in any feasiblecombination.

In one implementation, a first sensitivity associated with the updatedvirtual measurement can be smaller than a second sensitivity associatedwith the initial virtual measurement. In another implementation, the oneor more predictive models can include an oil well predictive model and apipeline predictive model. In yet another implementation, at least oneof the one or more measurement is at a manifold of the oil field.

In one implementation, the manifold predictive model is associated witha first manifold in the oil field, the first manifold coupled to aplurality of oil wells in the oil field. The one or more predictivemodels includes one or more of flow models associated with pipesconnecting the first manifold to the plurality of oil wells, andphysical equations and/or sensor measurements associated with one ormore of the plurality of oil wells.

In one implementation, the manifold predictive model is configured tocalculate a probabilistic estimation indicative of an operating range ofa flow rate associated with one or more of oil, gas and water from thefirst manifold. In another implementation, the calculation ofprobabilistic estimation of flow rates is based on one or more sensormeasurements at the first manifold. In yet another implementation, themanifold predictive model is configured to receive data characterizingthe detected oil field parameter and generate an updated virtualmeasurement.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, causes at least one data processor to performoperations herein. Similarly, computer systems are also described thatmay include one or more data processors and memory coupled to the one ormore data processors. The memory may temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including aconnection over a network (e.g. the Internet, a wireless wide areanetwork, a local area network, a wide area network, a wired network, orthe like), via a direct connection between one or more of the multiplecomputing systems, etc.

These and other capabilities of the disclosed subject matter will bemore fully understood after a review of the following figures, detaileddescription, and claims.

BRIEF DESCRIPTION OF THE FIGURES

These and other features will be more readily understood from thefollowing detailed description taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a schematic illustration of an oil field;

FIG. 2 illustrates an exemplary estimation of handoff flowrate;

FIG. 3 illustrates a representation of an exemplary well and lift model;

FIG. 4 illustrates exemplary gas flow virtual measurements of wellmodels for naturally flowing reservoir and gas gifted wells;

FIG. 5 illustrates an exemplary pair plot of manifold pressures;

FIG. 6 illustrates virtual output of flow meter determined forsimulation points in FIG. 5;

FIG. 7 illustrates an exemplary sensitivity plot for FIGS. 5 and 6;

FIG. 8 illustrates measurements and uncertainties associated with themeasurements at the three manifolds identified in FIG. 2;

FIG. 9 illustrates a probabilistic estimation of a flowrate associatedwith the manifold C in FIG. 2;

FIG. 10 illustrates a probabilistic estimation of a flowrate associatedwith the manifold A1 in FIG. 2;

FIG. 11 illustrates flowrates at A1 and NE1 manifolds of FIG. 2;

FIG. 12 illustrates flowrates at A1, NE1 and NE2 manifolds when nomeasurements are enforced;

FIG. 13 illustrates flowrates at A1, NE1 and NE2 manifolds whenmeasurement at A1 is enforced;

FIG. 14 illustrates flowrates at A1, NE1 and NE2 manifolds whenmeasurements at A1 and NE2 are enforced;

FIG. 15 illustrates flowrates at A1, NE1 and NE2 manifolds whenmeasurements at A1, NE1 and NE2 are enforced; and

FIG. 16 is a flow chart of an exemplary method for determining aprospective sensor location for improving a virtual measurement.

DETAILED DESCRIPTION

Simulations can be used to estimate variables of processes that cannotbe directly or indirectly measured by sensors, or are measured bysensors that are inaccurate (e.g., due to wear and tear). Estimation ofprocess variables (referred to as virtual measurement) can be desirable,for instance, when installing and/or replacing a sensor in an oil fieldrequires shutting down oil production (e.g., which can result in revenuelosses). However, such simulations may be slow, inaccurate, and/or maynot capture the operating principles of the process. Accordingly,systems and corresponding methods for improved virtual measurement areprovided. Improvements in virtual measurement process can be achieved,for example, based on the realization that oil wells in an oil field (oran oil production facility) can be interconnected by a system ofpipelines, via oil and gas industrial machines (e.g., valves) and thelike. Therefore, measurement from sensors distributed over the oilfields can be used to generate/calibrate predictive model for virtualmeasurements and/or determine uncertainty (or accuracy) associated withthe virtual measurements.

Virtual measurements by predictive models can result in virtual processparameters indicative of various properties of an oil field (e.g., oilflow, manifold pressure, etc.). The oil field can include multipleclusters of oil wells. The output of the oil wells (e.g., oil, gas,water or a mixture thereof) can be connected via a system of pipelines.For example, output of oil wells in a cluster can be transferred to acluster manifold where the various outputs can be combined and/orseparated into oil, gas and water. Sensors (e.g., pressure sensors, flowsensors, etc.) can be deployed at various locations in the oil fields todetect pressure and flow of output from an oil field (e.g., oil output).

Virtual measurement and sensor measurement for a given oil fieldparameter may differ. This can result from measurement errors associatedwith predictive models, measurement sensors or both. For example,sensors deployed in the oil field may be old and may not provideaccurate measurement. Additionally or alternately, predictive models maynot be calibrated, which can result in inaccurate virtual measurements.Errors in sensor and virtual measurements (or discrepancy between them)can result in erroneous determination of oil production from an oilfield and can lead to loss in revenue. Therefore, it is desirable todevelop a predictive model that can improve the measurement accuracy ofoil production (e.g., by calculation of virtual process parameters).

FIG. 1 is a schematic illustration of an oil field 100. The oil field100 can include oil clusters 110 and 120 comprising multiple oil wells.The oil cluster 110 can include multiple oil wells 112, 114, 116, andthe output of the oil wells (e.g., a multiphase fluid including oil, gasand water) can be transferred to a cluster manifold 118 via pipes 102,104 and 106. The oil cluster 120 can include multiple oil wells 122,124, 126, and the output of the oil wells (e.g., a multiphase fluidincluding oil, gas and water) can be transferred to a cluster manifold128 via pipes 132, 134 and 136. At the cluster manifold 118, outputs ofthe oil wells 112, 114, 116 can be combined. The combined outputs fromclusters 110 and 120 can be transferred to a cluster manifold 148 viapipes 142 and 144, respectively. Output from the cluster manifold 148can be transferred to downstream facilities (e.g. gas processingfacilities, oil facilities, etc.). The manifolds 118, 128 and 148 caninclude a separator that can separate various components of themultiphase fluid (e.g., oil, gas and water). For example, clustermanifold 148 can separate fluid output from wells upstream into an oiloutput 152, a gas output 154 and water output 156 (also referred to ashand off outputs). The oil output 152 can be transferred to an oilprocessing facility and the gas output 154 can be transferred to a gasprocessing facility. The oil and gas processing facilities can includeexport sensors (e.g., sensors that can be more accurate than the sensorsin the oil field) that can detect the flow rates of one or more of theoil output 152, the gas output 154 and the water output 156.

During the initial phase of production, oil wells can be naturallyflowing and fluid (e.g., oil) oozes out of the well due to pressure atthe reservoir that can lift the oil naturally to the surface. Thereservoir pressure can decrease, for example as the oil well ages, andan artificial lift mechanism (e.g., Electric submersible pumps, GasLift, Gas Injection, Rod Lift Pumps etc . . . ) may need to be used toextract oil. For example, the wells 112, 116 and 122, 126 can includepumps to extract oil. The wells can also include one or more flowsensors to measure the fluid output of the well, pressure sensors (e.g.,to measure well head pressure) and sensors to detect the composition ofthe fluid output. These sensors can be located at one or more locationsin the pipes (e.g., 102, 106, 132, 136, etc) and manifolds 118, 128 and148.

It can be desirable to maintain a continuous production of oil (e.g., apredetermined flow of output from the cluster manifold 148) and preventunplanned shutdowns. Replacing a sensor in the oil field that isproducing inaccurate measurement can lead to downtime which is not bedesirable. However, predictive models can be developed for the varioussensors that can calculate virtual parameters associated with thesensors. In some implementations, virtual parameters can be calculatedat a location where no sensor is present (e.g., virtual pressuredetection at a location where no pressure sensor is present). Thepredictive models can be calibrated based on various sensor measurementsin the oil field, physical model of sensors, physical model of oilwells, physical model of pipes, etc. Because the oil wells in the oilfield are interconnected via a network of pipes, sensor measurement atvarious locations in the oil fields can be used to calibrate apredictive model (e.g., predictive model for a sensor measurement or aprocess) in the oil field (e.g., a predictive model of a sensor remotefrom the measurement location).

In some implementations, a manifold predictive model can be generated(e.g., for manifolds 118, 128, 148, etc.). The manifold predictive modelcan be generated based on predictive models of oil wells and pipes thatare upstream (or downstream) from the manifold. For example, manifoldpredictive model for manifold 118 can be based on models associated withwells 112 116 and pipes 102 106. The manifold predictive model can alsobe based on one or more sensor measurements taken upstream from themanifold (e.g., change in pressure of a fluid and/or change in phase ofthe fluid flowing along a segment of a pipeline upstream from themanifold). In some implementations, the manifold predictive model can bebased on (or calibrated) sensor measurements downstream from themanifold. In some implementations, the manifold predictive model caninclude a thermodynamic model based on inenthalpic mixing of the fluidoutputs from the various wells upstream from the manifold. In someimplementations, a manifold can include a separator that can separatefluid arriving at the manifold from the wells upstream from themanifold. For example, the separator can separate oil, gas and waterfrom the multiphase fluid arriving at the manifold. In someimplementations, the manifold predictive model can calculate the flowrates of oil, gas, and water that are obtained from the above-mentionedseparation.

The oil production and transfer system (e.g., including oil wells, oiland gas industrial machines [e.g., valves], oil carrying pipelines, andthe like) can route output from different wells to be combined and/orre-routed through different production facilities as desired. In someimplementations, two types of measurements can be available fordetermining handoff flowrates (e.g., outputs 152-156) from oil fields. Afirst measurement can be obtained from a physical sensor that can havean uncertainty (e.g., 8%), and a second measurement can be obtained fromhand allocation calculations. The discrepancy between these measurementscan be problematic for customers. A system of systems modeling approach(e.g., generation of predictive model for a manifold) can enable anobjective mathematically sound estimate for measurements in the oilfield. Such a system of systems model can be based on physical equationsassociated with oil wells, surface flow networks (e.g., includingpipelines in the oil field) to infer unknowns at one location (virtualmeasurements) using sensor measurements at other locations in thenetwork.

In some implementations, sensor measurements can be performed atdifferent locations in the oil field, but the measurements may not beaccurate (e.g., due to errors in compositional information of oil welloutputs, unexpected deterioration of oil well, an overdue calibrationand neglect of instrumentation, etc.). Calibration of sensors canrequire dedicated access to the pipeline that can obstruct oilproduction.

Uncertainty in determining the true amount of hydrocarbons (e.g., oil,gas, etc.) that are exported through the pipes can result in lostrevenue (e.g., in the tune of billions of dollars per year for a largeoil field). For example, an error in measurement accuracy (e.g.,estimated difference between the mean value of flow of hydrocarbon andthe actual value of hydrocarbon flow) can result in lost revenue.Measurement uncertainty, on the other hand, can be indicative of a rangeof variation around an estimate. It can be desirable that themeasurement uncertainty is small. Measurement uncertainty can beindicative of how the system is responding to valve positionadjustments. Knowledge of measurement uncertainty can lead to insightinto the state of different components of the network, degradation ofinstrumentation, etc.

Multiple sensors in the oil field that have poor accuracy. However, thefact that the different parts of the oil field are connected in anetwork can be used with the physical equations of various components ofthe oil field to make accurate estimates of production of oil and/orgas. The disparate measurements can be considered as data which whenconnected through a physical model of the network can provide “accurate”insights.

Sensor measurements can be made at certain cumulative locations of theproduction network (e.g., at a customer's facility that receives oiloutput 152). But these measurements may not be used to increase theaccuracy of handoff flowrate estimates. FIG. 2 illustrates an exemplaryestimation of handoff flowrate. The estimation can be based on computeduncertainty of three oil flowrate measurements at different manifolds(A1, NE1 and NE2) shown in FIG. 2.

FIG. 3 illustrates a representation of an exemplary well and lift model.The blue dots 302-310 in FIG. 3 refer to various well and the associatedlift mechanism. The predictive models for the reservoir, pump, fluidflow through horizontal and vertical pipe sections, manifold pressure atsurface can be built and used to characterize how the production wouldbe affected by valve settings at the top of the well. The variousmeasurement points 312-320 (e.g., sensor locations) are shown in FIG. 3.For example, the measurement points can be located at well-heads,manifolds (e.g., for detecting multiphase flow), etc. In someimplementations, these measurements can be around 2-3% accurate and canfeed into the facility where the compressors and flow components aresplit into a single phase flow where measurement accuracy is about 1%.

Reduced order models of the wells can be built and calibrated based onsensor measurements in the oil field. The reduced order models can be adrop-in replacement to perform several (e.g., thousands) of calculationsfor different production estimation scenarios. FIG. 4 illustratesexemplary gas flow virtual measurements of well models for naturallyflowing reservoir and gas lifted wells.

The flow from different wells can be combined in a manifold andtransported through surface pipelines. Pressure loss models and phasechange models can predict the state of the fluid being transferredthrough the pipelines. Combine fluid flow models using manifolds andsplit flow can be generated. These models can utilize thermodynamics tomodel the inenthalpic mixing process to accurately assess the enthalpyof the inlet and exit streams along with the pressure, flow andcomposition of the fluid. In some implementations, once the differentcomponents of the oil field are connected, parameters of control (e.g.,manifold pressures) and multiple measurement at different locations(e.g., well head and manifold flow measurements) can be used in a MonteCarlo sweep of the entire design space to understand/predictcharacteristics of the oil field. Monte Carlo sweep can include repeatedrandom sampling to obtain numerical results. For example, randomness canbe used to solve problems that may be deterministic in nature.

FIG. 5 illustrates an exemplary pair plot of manifold pressures (firstfive columns) versus the resulting production of liquid and gaseouscomponents (shown in last two columns). For each of the simulation pointin FIG. 5, the predictive model of the oil field can be evaluated todetermine the virtual output at the flow meter locations (e.g., atvarious manifolds). FIG. 6 illustrates virtual output of flow meterdetermined for the simulation points in FIG. 5.

In some implementations, more than 10000 hybrid simulations ofpredictive models can be executed in the order of seconds to determinethe data for FIGS. 5 and 6. These figures can provide information onsensitivities of the manifold pressures to overall production (e.g., oiland/or gas production) and/or the optimal instrumentation locations(e.g., sensor location) in order to achieve benefit (e.g., maximalbenefit) from an accurate sensor measurement in the oil field.

While system of systems models (e.g., generated using predictive modelsof various components of the oil field) are great at estimating missinginputs, they may require several inputs (e.g., above a threshold value)to predict outputs. If no inputs are available, the model can be used tounderstand the most critical location where a sensor is needed. Missingsensor locations can be inferred from the sensitivity plots.

FIG. 7 illustrates an exemplary sensitivity plot for FIGS. 5 and 6. Thesensitivity plot can be used in identifying one or more locations (e.g.,most important location) for adding sensors in the oil field network.FIG. 7 can indicate that for estimating liquid production, it can bedesirable to have a sensor measurement in A1 manifold (see FIG. 2). Suchsensitivity charts can be useful for understanding relative importanceof each measurement to the accuracy of handoff flowrate estimation.

In some implementations, system of systems models can be created usingphysics based models of networks in the oil field, oil wells, artificiallift equipment etc. Such physics based models can be converted toreduced order machine learning models for faster execution. Such physicsor reduced order models can provide a frequentist view of the outputs(e.g., they provide a single set of outputs given a single set ofinputs). In some implementations, Bayesian methods can be used tounderstand probabilistic relationships between different variables inthe network and estimate uncertainties in estimates (e.g., in virtualmeasurement). Such probabilistic computations can be aided by the powerof cloud computing which can bring unprecedented speed to suchtechniques.

FIG. 8 illustrates measurements and uncertainties associated with themeasurements at the three manifolds identified in FIG. 2. The greenlines 802, 804 and 806 at the center can be indicative of measurementsperformed on the day of interest. Green bars 812-816 and 822-826 ateither side of the mean value are uncertainties based on historicalvalues (e.g., historical sensor values, historical virtual measurements,etc.). With these three measurements and a reduced-order system ofsystems model of the entire network, one can estimate probabilisticallythe expected handoff flowrate associated with the manifold C in FIG. 2(e.g., flow rate of oil output 152, gas output 154 water output 156,etc.).

FIG. 9 illustrates a probabilistic estimation 900 of a flowrateassociated with the manifold C in FIG. 2. The center red line 902 is ameasured value from handoff flowmeter (e.g., associated with thelocation C) and can serve as a reference. FIG. 9 illustrates that theestimated flow rate has a median value of 3498.5 barrel oil per day(BPD) with an uncertainty of 58 BPD or about 2% which is much smallerthan uncertainty of current measurement of 8%. The uncertainty can besmaller if measurement uncertainties of individual flowmeters werelower. The approach of probabilistic estimation of median value can bebetter (e.g., more accurate) than simply summing up production ratesfrom individual flow lines. While the above result indicate thatexisting sensors can be used with a probabilistic system of systemsmodel to achieve better accuracy, such system of systems models alsolead a number of other desirable outcomes.

In some implementations, if the sensor in the A1 manifold is notavailable, the system of systems model can be used to estimatemeasurements in this manifold. While this estimate can be less accurateand more uncertain than the previous scenario where an actualmeasurement is available in the manifold, it can allow for determinationof a reasonable value (e.g., accuracy above a threshold value) using therest of the network. The estimate for total handoff flowrate for oilhowever can have an uncertainty because of this missing measurement.

FIG. 10 illustrates a probabilistic estimation 1000 of a flowrateassociated with the manifold A1 in FIG. 2. The uncertainty in thisinstance, is still smaller than 8% uncertainty in spite of no actualmeasurement at the manifold A1. FIG. 11 illustrates flowrates at A1 andNE1 manifolds of FIG. 2. FIG. 11 indicates that it may be possible toestimate handoff flowrate with one flowmeter at the NE2 manifold. Theflowrates at A1 and NE1 can be estimated by the system of systems model.

FIG. 12 illustrates flowrates at A1, NE1 and NE2 manifolds when nomeasurements are enforced. FIG. 13 illustrates flowrates at A1, NE1 andNE2 manifolds when measurement at A1 is enforced. FIG. 14 illustratesflowrates at A1, NE1 and NE2 manifolds when measurements at A1 and NE2are enforced. FIG. 15 illustrates flowrates at A1, NE1 and NE2 manifoldswhen measurements at A1, NE1 and NE2 are enforced.

Systems and methods described in this application can provide severaladvantages and novelty. In some implementations, oil field networkarchitecture can allow for lego block style assembling of predictivemodels of components of the oil field. In some implementations,large-scale networks that include predictive models (e.g., HybridPhysics Models) of components that works seamlessly with other connectedcomponents can be created. The concept of a network can be used to tiedifferent disparate information (e.g., sensor measurements) acrossmultiple phases, flow, pressure and temperature.

In some instances, the network model can solve the production estimationproblem. Uncertainties across all or some nodes of the network can berolled up (e.g., combined). The network predictive model can becalibrated to match physical reality and can then be executed inprediction mode. If the virtual measurement from the network predictivemodel and sensor measurement diverge, the network predictive model canbe recalibrated. The network predictive model can suggest the bestplaces of observability for instrumentation (e.g., works with partialinstrumentation).

FIG. 16 is a flow chart of an exemplary method for determining aprospective sensor location for improving a virtual measurement. At 1602a manifold predictive model can be generated. The manifold predictivemodel can be generated for (or associated with) a given manifold in theoil field (e.g., manifold A1, A2, A3, etc.). In some implementations,the manifold predictive model can be generated based on predictivemodels (e.g., predetermined) associated with one or more portions of theoil field. The manifold predictive model can be based on predictivemodels of manifolds and/or oil well that are coupled to the givenmanifold (e.g., manifolds/oil well from which oil/gas/water flows intothe given manifold, manifolds/oil wells to which oil/gas/water flows outof the given manifold, etc.), physical equations of the coupledmanifolds/oil fields and pipes coupling the manifolds/oil wells to thegiven manifold. For example, the manifold predictive model of manifoldA1 in FIG. 2 can be based on predictive models, physical equations andsensor measurements at manifolds A2 and A3 (or manifolds/oil wellscoupled to the manifolds A2 and A3).

In some implementations, the manifold predictive model can be used tocalculate a probabilistic estimation indicative of an operating range ofa flow rate associated with one or more of oil, gas and water from thefirst manifold. For example, as illustrated in FIG. 10, the manifoldpredictive model can calculate a probabilistic estimation of flowrateassociated with manifold A1. The probabilistic estimation can include arange of flowrates at A1 and the probability associated with the variousflow rates. Additionally or alternately, a median flowrate value and anuncertainty around the medial flowrate value can also be calculated. Insome implementations, the calculation of probabilistic estimation offlow rates can be based on one or more sensor measurements at the givenmanifold (e.g., manifold A1). The sensor can allow for an actualmeasurement of flowrate which can be used to by the manifold model togenerate the probabilistic estimation. Presence of a sensor measurementcan reduce the uncertainty associated with the probabilistic estimation.Additionally or alternately, the probabilistic estimation can be used todetermine if the sensor at the given manifold is operating properly. Forexample, if the sensor measurement and the probabilistic estimation donot match (e.g., the difference between the sensor measurement and themedian of the probabilistic estimation is greater than a predeterminedvalue [e.g., one or more standard deviations]), it can be determinedthat the sensor needs to be replaced or recalibrated. The manifoldpredictive model can be configured to calculate an initial virtualmeasurement associated with an oil field comprising a plurality of oilwells. The manifold predictive model can be based on one or morepredictive models associated with one or more components of the oilfield.

At 1604, data characterizing one or more pressure measurements and flowmeasurements obtained in the oil field can be received. At 1606, aprospective sensor location of a first prospective sensor in the oilfield can be determined. The first prospective sensor can be configuredto detect an oil field parameter. The manifold predictive model can beconfigured to receive data characterizing the detected oil fieldparameter and generate an updated virtual measurement. At 1608, theprospective sensor location and the identity of the first prospectivesensor can be provided (e.g., in a graphical user interface displayspace).

Exemplary technical effects of the methods, systems, and devicesdescribed herein include, by way of non-limiting example, expediting thecalculation of virtual measurement values, for example, due toparallelization of the simulation. Further, applying an iterativealgorithm to the simulation of process flow algorithm can result inaccurate and robust determination of virtual measurement values.

Certain exemplary embodiments are described herein to provide an overallunderstanding of the principles of the structure, function, manufacture,and use of the systems, devices, and methods disclosed herein. One ormore examples of these embodiments are illustrated in the accompanyingdrawings. Those skilled in the art will understand that the systems,devices, and methods specifically described herein and illustrated inthe accompanying drawings are non-limiting exemplary embodiments andthat the scope of the present invention is defined solely by the claims.The features illustrated or described in connection with one exemplaryembodiment may be combined with the features of other embodiments. Suchmodifications and variations are intended to be included within thescope of the present invention. Further, in the present disclosure,like-named components of the embodiments generally have similarfeatures, and thus within a particular embodiment each feature of eachlike-named component is not necessarily fully elaborated upon.

Other embodiments are within the scope and spirit of the disclosedsubject matter. One or more examples of these embodiments areillustrated in the accompanying drawings. Those skilled in the art willunderstand that the systems, devices, and methods specifically describedherein and illustrated in the accompanying drawings are non-limitingexemplary embodiments and that the scope of the present invention isdefined solely by the claims. The features illustrated or described inconnection with one exemplary embodiment may be combined with thefeatures of other embodiments. Such modifications and variations areintended to be included within the scope of the present invention.Further, in the present disclosure, like-named components of theembodiments generally have similar features, and thus within aparticular embodiment each feature of each like-named component is notnecessarily fully elaborated upon.

The subject matter described herein can be implemented in digitalelectronic circuitry, or in computer software, firmware, or hardware,including the structural means disclosed in this specification andstructural equivalents thereof, or in combinations of them. The subjectmatter described herein can be implemented as one or more computerprogram products, such as one or more computer programs tangiblyembodied in an information carrier (e.g., in a machine-readable storagedevice), or embodied in a propagated signal, for execution by, or tocontrol the operation of, data processing apparatus (e.g., aprogrammable processor, a computer, or multiple computers). A computerprogram (also known as a program, software, software application, orcode) can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment. Acomputer program does not necessarily correspond to a file. A programcan be stored in a portion of a file that holds other programs or data,in a single file dedicated to the program in question, or in multiplecoordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification, includingthe method steps of the subject matter described herein, can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions of the subject matter describedherein by operating on input data and generating output. The processesand logic flows can also be performed by, and apparatus of the subjectmatter described herein can be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processor of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, (e.g., EPROM, EEPROM, and flash memorydevices); magnetic disks, (e.g., internal hard disks or removabledisks); magneto-optical disks; and optical disks (e.g., CD and DVDdisks). The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computer having a display device, e.g., aCRT (cathode ray tube) or LCD (liquid crystal display) monitor, fordisplaying information to the user and a keyboard and a pointing device,(e.g., a mouse or a trackball), by which the user can provide input tothe computer. Other kinds of devices can be used to provide forinteraction with a user as well. For example, feedback provided to theuser can be any form of sensory feedback, (e.g., visual feedback,auditory feedback, or tactile feedback), and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The techniques described herein can be implemented using one or moremodules. As used herein, the term “module” refers to computing software,firmware, hardware, and/or various combinations thereof. At a minimum,however, modules are not to be interpreted as software that is notimplemented on hardware, firmware, or recorded on a non-transitoryprocessor readable recordable storage medium (i.e., modules are notsoftware per se). Indeed “module” is to be interpreted to always includeat least some physical, non-transitory hardware such as a part of aprocessor or computer. Two different modules can share the same physicalhardware (e.g., two different modules can use the same processor andnetwork interface). The modules described herein can be combined,integrated, separated, and/or duplicated to support variousapplications. Also, a function described herein as being performed at aparticular module can be performed at one or more other modules and/orby one or more other devices instead of or in addition to the functionperformed at the particular module. Further, the modules can beimplemented across multiple devices and/or other components local orremote to one another. Additionally, the modules can be moved from onedevice and added to another device, and/or can be included in bothdevices.

The subject matter described herein can be implemented in a computingsystem that includes a back-end component (e.g., a data server), amiddleware component (e.g., an application server), or a front-endcomponent (e.g., a client computer having a graphical user interface ora web browser through which a user can interact with an implementationof the subject matter described herein), or any combination of suchback-end, middleware, and front-end components. The components of thesystem can be interconnected by any form or medium of digital datacommunication, e.g., a communication network. Examples of communicationnetworks include a local area network (“LAN”) and a wide area network(“WAN”), e.g., the Internet.

Approximating language, as used herein throughout the specification andclaims, may be applied to modify any quantitative representation thatcould permissibly vary without resulting in a change in the basicfunction to which it is related. Accordingly, a value modified by a termor terms, such as “about” and “substantially,” are not to be limited tothe precise value specified. In at least some instances, theapproximating language may correspond to the precision of an instrumentfor measuring the value. Here and throughout the specification andclaims, range limitations may be combined and/or interchanged, suchranges are identified and include all the sub-ranges contained thereinunless context or language indicates otherwise.

What is claimed is:
 1. A method comprising: generating a manifoldpredictive model configured to calculate an initial virtual measurementassociated with an oil field comprising a plurality of oil wells, themanifold predictive model based on one or more predictive modelsassociated with one or more components of the oil field; receiving datacharacterizing one or more pressure measurements and flow measurementsobtained in the oil field, determining a prospective sensor location ofa first prospective sensor in the oil field, the first prospectivesensor configured to detect an oil field parameter; and providing theprospective sensor location and/or the identity of the first prospectivesensor.
 2. The method of claim 1, wherein the manifold predictive modelis associated with a first manifold in the oil field, the first manifoldcoupled to a plurality of oil wells in the oil field, and wherein theone or more predictive models includes one or more of flow modelsassociated with pipes connecting the first manifold to the plurality ofoil wells, and physical equations and/or sensor measurements associatedwith one or more of the plurality of oil wells.
 3. The method of claim2, wherein the manifold predictive model is configured to calculate aprobabilistic estimation indicative of an operating range of a flow rateassociated with one or more of oil, gas and water from the firstmanifold.
 4. The method of claim 3, wherein the calculation ofprobabilistic estimation of flow rates is based on one or more sensormeasurements at the first manifold.
 5. The method of claim 1, whereinthe manifold predictive model is configured to receive datacharacterizing the detected oil field parameter and generate an updatedvirtual measurement.
 6. The method of claim 5, wherein a firstsensitivity associated with the updated virtual measurement is smallerthan a second sensitivity associated with the initial virtualmeasurement.
 7. The method of claim 1, wherein at least one of the oneor more pressure measurements and flow measurements are obtained at amanifold of the oil field.
 8. A system comprising: at least one dataprocessor; memory coupled to the at least one data processor, the memorystoring instructions to cause the at least one data processor to performoperations comprising: generating a manifold predictive model configuredto calculate an initial virtual measurement associated with an oil fieldcomprising a plurality of oil wells, the manifold predictive model basedon one or more predictive models associated with one or more componentsof the oil field; receiving data characterizing one or more pressuremeasurements and flow measurements obtained in the oil field,determining a prospective sensor location of a first prospective sensorin the oil field, the first prospective sensor configured to detect anoil field parameter; and providing the prospective sensor locationand/or the identity of the first prospective sensor.
 9. The system ofclaim 8, wherein the manifold predictive model is associated with afirst manifold in the oil field, the first manifold coupled to aplurality of oil wells in the oil field, and wherein the one or morepredictive models includes one or more of flow models associated withpipes connecting the first manifold to the plurality of oil wells, andphysical equations and/or sensor measurements associated with one ormore of the plurality of oil wells.
 10. The system of claim 9, whereinthe manifold predictive model is configured to calculate a probabilisticestimation indicative of an operating range of a flow rate associatedwith one or more of oil, gas and water from the first manifold.
 11. Thesystem of claim 10, wherein the calculation of probabilistic estimationof flow rates is based on one or more sensor measurements at the firstmanifold.
 12. The system of claim 8, wherein the manifold predictivemodel is configured to receive data characterizing the detected oilfield parameter and generate an updated virtual measurement.
 13. Thesystem of claim 12, wherein a first sensitivity associated with theupdated virtual measurement is smaller than a second sensitivityassociated with the initial virtual measurement.
 14. The system of claim12, wherein at least one of the one or more pressure measurements andflow measurements are obtained at a manifold of the oil field.
 15. Acomputer program product comprising a machine-readable medium storinginstructions that, when executed by at least one programmable processor,cause the at least one programmable processor to perform operationscomprising: generating a manifold predictive model configured tocalculate an initial virtual measurement associated with an oil fieldcomprising a plurality of oil wells, the manifold predictive model basedon one or more predictive models associated with one or more componentsof the oil field; receiving data characterizing one or more pressuremeasurements and flow measurements obtained in the oil field,determining a prospective sensor location of a first prospective sensorin the oil field, the first prospective sensor configured to detect anoil field parameter; providing the prospective sensor location and/orthe identity of the first prospective sensor.
 16. The computer programproduct of claim 15, wherein the manifold predictive model is associatedwith a first manifold in the oil field, the first manifold coupled to aplurality of oil wells in the oil field, and wherein the one or morepredictive models includes one or more of flow models associated withpipes connecting the first manifold to the plurality of oil wells, andphysical equations and/or sensor measurements associated with one ormore of the plurality of oil wells.
 17. The computer program product ofclaim 16, wherein the manifold predictive model is configured tocalculate a probabilistic estimation indicative of an operating range ofa flow rate associated with one or more of oil, gas and water from thefirst manifold.
 18. The computer program product of claim 17, whereinthe calculation of probabilistic estimation of flow rates is based onone or more sensor measurements at the first manifold.
 19. The computerprogram product of claim 15, wherein the manifold predictive model isconfigured to receive data characterizing the detected oil fieldparameter and generate an updated virtual measurement.
 20. The computerprogram product of claim 19, wherein a first sensitivity associated withthe updated virtual measurement is smaller than a second sensitivityassociated with the initial virtual measurement.